Multi-device high-resolution audio stream intelligent pushing method, device, equipment and storage medium

Abstract

The invention discloses a multi-device high-resolution audio stream intelligent pushing method and device, equipment and a storage medium, which are applied to a multi-device audio system, and the method comprises the following steps: acquiring a current audio stream format and adjusting system working parameters; dynamically adjusting a multi-stage buffer area based on a tone quality priority strategy; operation parameters of multiple devices are synchronously monitored in real time, and data are sent in advance for the device with large delay based on the delay characteristic and the synchronization capability of each device in the system; and dynamically switching system service levels based on the quality of the current audio stream. A protection strategy can be flexibly adjusted according to different audio formats through a format perception pre-detection thread mechanism; the sound quality is kept to the greatest extent while the stability is ensured by the multi-stage buffer region protection with the sound quality priority; a multi-device coordination and timing monitoring mechanism ensures perfect cooperation with a third-party device, and the stability challenge in high-resolution audio transmission is comprehensively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-device audio pushing, and in particular to an intelligent pushing method, device, equipment and storage medium for multi-device high-resolution audio streams. Background Art

[0002] Modern high-end audio stream devices, based on advanced wireless transmission technologies and high-quality digital-to-analog converters, support multiple input / output interfaces and high-resolution audio formats. However, in practical applications, they still face severe stability challenges. High-resolution audio streams require high bandwidth, and the data volume of the 24-bit / 192kHz format is 4-8 times that of standard audio quality, making it extremely easy to cause buffer overflows and system crashes during network fluctuations. The multi-room audio function requires precise synchronization between devices. When networking with third-party devices, protocol differences and improper resource coordination often lead to synchronization failures. When switching between HDMI ARC and multiple input sources, the buffer management strategy is simple and fails to effectively handle format conversion and cache cleaning. In addition, current stream devices often handle abnormal situations in an all-or-nothing manner and lack a graceful degradation mechanism to ensure service continuity. In the multi-room audio synchronization scenario, the cross-device coordination mechanism is imperfect, and stability issues are particularly prominent when integrating with third-party smart speakers. Improper buffer management during the switching between HDMI ARC and multiple input sources results in audio interruptions, seriously affecting the user experience of high-end audio devices. Summary of the Invention

[0003] The present invention provides an intelligent pushing method, device, equipment and storage medium for multi-device high-resolution audio streams. Through the format-aware pre-detection thread mechanism, the protection strategy can be flexibly adjusted according to different audio formats; the multi-level buffer protection with sound quality priority can retain the sound quality to the greatest extent while ensuring stability; the multi-device coordination and timing monitoring mechanism ensures perfect cooperation with third-party devices, comprehensively solving the stability challenges in high-resolution audio transmission.

[0004] In the first aspect of the present invention, an intelligent pushing method for multi-device high-resolution audio streams is provided, which is applied to a multi-device audio system and includes the following steps: Obtain the current audio stream format and adjust the system working parameters; Dynamically adjust the multi-level buffer based on the sound quality priority strategy; Real-time synchronously monitor the operating parameters of multiple devices, and send data in advance to devices with larger delays based on the delay characteristics and synchronization capabilities of each device in the system; Dynamically switch the system service level based on the quality of the current audio stream.

[0005] Preferably, obtaining the current audio stream format and adjusting the system working parameters further includes: When the audio session is initialized, the system creates an adapted pre-detection thread according to the characteristics of the audio format; Set the priority and sampling frequency based on the characteristics of the audio format; Establish an efficient communication channel with the main processing thread during initialization, and optimize the communication strategy for Wi-Fi and / or Bluetooth characteristics.

[0006] Preferably, obtaining the current audio stream format and adjusting the system working parameters further includes: Dynamically adjust the verification strategy and threshold according to the currently transmitted audio format, and configure advanced verification for high-resolution audio. The advanced verification includes bit-depth consistency check and sampling rate stability analysis; Allocate core processor and memory resources for the audio processing and pre-detection threads, and dynamically adjust the memory allocation strategy according to the current audio format.

[0007] Preferably, dynamically adjusting the multi-level buffer based on the sound quality priority strategy further includes: Calculate the applicable buffer size according to the current audio stream format and the buffer adjustment calculation formula; Create a dedicated buffer configuration based on the data characteristics of the audio stream format; Switch to the dedicated buffer.

[0008] Preferably, dynamically adjusting the multi-level buffer based on the sound quality priority strategy further also includes: Based on the digital-to-analog converter supply guarantee mechanism, adjust the buffer and processing priority when the digital-to-analog converter data is lower than the preset value, so as to provide a constant data stream for the digital-to-analog converter.

[0009] Preferably, real-time synchronously monitor the operating parameters of multiple devices, and based on the latency characteristics and synchronization capabilities of each device in the system, send data in advance to the device with a larger latency. Further includes Real-time collect the current synchronization deviation of multiple devices. When the deviation exceeds the preset value, calculate the corresponding adjustment amount according to the deviation and perform synchronization; Set different watchdog timeout thresholds for different audio processing links, establish a hierarchical response mechanism, and perform recovery in combination with the characteristics of the audio content; Define the key indicators specific to multi-room audio, periodically evaluate the health status of the entire multi-room system, predict potential risks, and adaptively adjust the scoring threshold according to different device types and network environments.

[0010] Preferably, dynamically switching the system service level based on the quality of the current audio stream further includes: Determine the upper limit of the device capabilities, and preliminarily evaluate the applicable level according to the resource availability; Consider the original format limitations, ensure that it does not exceed the device capabilities, and return the finally determined service level.

[0011] Preferably, the dynamic switching of the system service level based on the quality of the current audio stream further includes: Establish a resource allocation prediction model to define priority levels and resource demand models for different service types, and implement a mechanism for resource borrowing and return between services; Based on the stability assessment of multiple parameters, including comprehensive analysis of network quality, processing capacity, and memory availability, establish a historical fluctuation pattern library to predict resource stability trends, and design a progressive service upgrade path to ensure a smooth transition.

[0012] Preferably, the method further includes: Identify and utilize the key features of Wi-Fi 6 and the features of Bluetooth 5.3 to achieve protocol adaptive switching, and intelligently select between Wi-Fi and Bluetooth; Establish a protocol capability mapping library to record the audio formats and features supported by each protocol version, optimize the data processing flow for different protocols, achieve unified internal processing in the protocol conversion layer, and maintain health scores and tuning parameters for each protocol; Identify the device type and processing capacity, establish a unified virtual clock system to solve the problem of inconsistent time bases for different devices, and design a hierarchical synchronization strategy to optimize the synchronization method for different network environments.

[0013] Preferably, the method further includes: Establish an input source characteristic database to record the latency characteristics, jitter characteristics, and format support of each input source, design an intelligent buffer pre-filling strategy, prepare new source data before switching, achieve intelligent conversion point identification based on audio content, and select the best switching timing; Establish a digital-to-analog converter characteristic model to accurately capture the performance characteristics and optimal operating parameters of the digital-to-analog converter, implement dynamic clock jitter management, design an intelligent data supply strategy to ensure that the digital-to-analog converter always operates at the best operating point, and optimize the parameter configuration of the digital-to-analog converter for different audio formats; Create an independent interactive processing thread, strictly isolate it from the audio processing thread, implement dynamic adjustment of resource priorities to ensure that the audio processing always obtains sufficient resources, and design a lightweight interface update mechanism to minimize the occupation of system resources.

[0014] Preferably, the method further includes: Establish an anonymous user behavior data collection mechanism, implement a pattern recognition algorithm to extract user habits from historical data, create a prediction model to predict the user's next operation, and pre-optimize the system resource allocation and buffering strategy based on the prediction; Design a lightweight performance monitoring framework to collect key metrics including crash rate and buffer underrun events, enabling the data aggregation and analysis system to identify common problems from a global perspective, optimize default parameter configurations based on collective data, and establish a database of abnormal situations for different device types and usage scenarios; Define a set of adjustable parameters at the device level and session level, implement a parameter effect evaluation mechanism to quantify the effects of different parameter configurations, explore better parameters based on an adaptive search algorithm while ensuring stability, and construct a device characteristic parameter library to optimize configurations for different hardware.

[0015] Preferably, the method further includes: Construct a database of streaming media service characteristics, record the characteristics of APIs on each platform, audio formats, and transmission protocols, design dedicated buffering and pre-reading strategies for different services, implement service-specific error recovery mechanisms, and establish a service switching mechanism to seamlessly transition between different streaming media services; Analyze the audio stream content, quickly identify audio characteristics, optimize the decoding strategy and resource allocation for different audio formats, construct a format conversion optimization matrix, and establish a format-specific quality assurance parameter set to maximize the audio quality performance; Implement an optimized layer for advanced audio transmission protocols to ensure seamless integration with the core system, optimize interoperability with professional audio devices, and implement professional clock synchronization strategies.

[0016] The second aspect of the present invention provides a multi-device high-resolution audio stream intelligent push device, including: A pre-detection module for obtaining the current audio stream format and adjusting the system working parameters; A buffer management module for dynamically adjusting multi-level buffers based on the audio quality priority strategy; A multi-device coordination module for real-time synchronously monitoring the operating parameters of multiple devices and sending data in advance to devices with larger delays based on the delay characteristics and synchronization capabilities of each device in the system; A service level management module for dynamically switching the system service level based on the quality of the current audio stream.

[0017] The third aspect of the present invention provides an electronic device, including: a memory for storing a processing program; a processor that, when executing the processing program, implements the multi-device high-resolution audio stream intelligent push method described in any one of the above.

[0018] The fourth aspect of the present invention provides a computer-readable storage medium, on which instructions are stored, characterized in that when the instructions are executed by a processor, the multi-device high-resolution audio stream intelligent push method described in any one of the above is implemented.

[0019] In the technical solution provided by the present invention, through the format-aware pre-detection thread mechanism, the audio stream format (such as bit rate, sampling rate, number of channels, etc.) is dynamically identified and the system working parameters (such as decoder configuration, clock frequency, etc.) are automatically adjusted to achieve fast compatibility with multiple audio sources, avoid playback interruption or sound quality loss caused by format mismatch, and at the same time improve the utilization rate of system resources. It can flexibly adjust the protection strategy according to different audio formats; the multi-level buffer protection with sound quality priority maximally retains the sound quality while ensuring stability; the multi-device coordination and timing monitoring mechanism ensures perfect cooperation with third-party devices. Based on the dynamic adjustment of multi-level buffers (such as network transmission buffer, decoding buffer, playback buffer), while ensuring the continuity of audio, more buffer resources are preferentially allocated to high-quality audio streams to reduce audio stuttering or decoding errors caused by insufficient buffering and ensure the stability of sound quality. By real-time monitoring parameters such as the CPU load, network latency, and decoding ability of multiple devices, data is pushed in advance for high-latency devices (such as terminals with low performance or devices with unstable networks), and combined with timestamp alignment and dynamic scheduling algorithms, millisecond-level synchronous playback among multiple devices is achieved, avoiding problems such as out-of-sync sound and picture or failure of cooperation between devices. In addition, according to the audio stream quality (such as packet loss rate, jitter, signal-to-noise ratio) and device status (such as bandwidth occupancy, memory usage), the system service level is dynamically adjusted (such as downgrading from lossless sound quality to lossy compression format), and the continuity of the audio stream is preferentially guaranteed in a complex network environment (such as Wi-Fi interference, mobile network fluctuations), balancing sound quality and transmission reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of an embodiment of the electronic device in this embodiment; Figure 2 Flow schematic diagram of the intelligent push method for high-resolution audio streams of multiple devices of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The present invention provides an intelligent push method, system, device and storage medium for high-resolution audio streams of multiple devices, which effectively solves the problem of reopening the capture when the audio input player switches modes through a persistent audio capture session and a dynamic audio processing pipeline.

[0022] In the description, claims and above-mentioned drawings of the present invention, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the term "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] This embodiment provides a multi-device high-resolution audio stream intelligent push method, which is applied to a multi-device audio system and includes the following steps: Obtain the current audio stream format and adjust the system working parameters; Dynamically adjust the multi-level buffer based on the sound quality priority strategy; Real-time synchronously monitor the operating parameters of multiple devices, and send data in advance to the devices with larger delays based on the delay characteristics and synchronization capabilities of each device in the system; Dynamically switch the system service level based on the quality of the current audio stream.

[0024] In this embodiment, through the format-aware pre-detection thread mechanism, by dynamically identifying the audio stream format (such as bit rate, sampling rate, number of channels, etc.) and automatically adjusting the system working parameters (such as decoder configuration, clock frequency, etc.), it realizes the fast compatibility with multiple audio sources, avoids playback interruption or sound quality loss caused by format mismatch, and at the same time improves the system resource utilization rate. It can flexibly adjust the protection strategy according to different audio formats; the multi-level buffer protection with sound quality priority maximally retains the sound quality while ensuring stability; the multi-device coordination and timing monitoring mechanism ensures perfect cooperation with third-party devices. Based on the dynamic adjustment of multi-level buffers (such as network transmission buffer, decoding buffer, playback buffer), while ensuring the audio continuity, it preferentially allocates more buffer resources for high-quality audio streams, reduces audio stuttering or decoding errors caused by insufficient buffering, and ensures the sound quality stability. By real-time monitoring parameters such as the CPU load, network latency, and decoding ability of multiple devices, it pushes data in advance for high-latency devices (such as terminals with low performance or devices with unstable networks), and combines timestamp alignment and dynamic scheduling algorithms to achieve millisecond-level synchronous playback among multiple devices, avoiding problems such as out-of-sync sound and picture or collaborative failure between devices. In addition, according to the audio stream quality (such as packet loss rate, jitter, signal-to-noise ratio) and device status (such as bandwidth occupancy, memory usage), it dynamically adjusts the system service level (such as downgrading from lossless sound quality to lossy compression format), and preferentially ensures the coherence of the audio stream in a complex network environment (such as Wi-Fi interference, mobile network fluctuations), balancing sound quality and transmission reliability.

[0025] Preferably, obtaining the current audio stream format and adjusting the system working parameters further includes: When the audio session is initialized, the system creates an adapted pre-detection thread according to the audio format characteristics; Set the priority and sampling frequency based on the audio format characteristics; Establish an efficient communication channel with the main processing thread during initialization, and optimize the communication strategy for Wi-Fi and / or Bluetooth characteristics.

[0026] Preferably, by calling the FormatAwarePreDetectionInit(audioFormat) function, the system actively analyzes the audio format (such as sampling rate, number of channels, compression encoding type) during the audio session initialization and creates a dedicated pre-detection thread. For example: start a hardware decoding preloading thread for high-bitrate lossless audio (such as FLAC) to reduce the real-time decoding delay; enable lightweight soft decoding pre-detection for low-complexity audio (such as AAC-LC) to reduce resource occupancy. Pre-place the audio format recognition and processing logic to shorten the initialization time by more than 30% and avoid stuttering at the beginning of playback.

[0027] The specific initialization process is as follows: 1. When the audio session is initialized, the system calls `FormatAwarePreDetectionInit(audioFormat)` according to the audio format characteristics to create an adapted pre-detection thread.

[0028] 2. Differentiated priorities and sampling frequencies are set for audio of different resolutions, and higher resolutions are assigned higher priorities and more frequent sampling rates.

[0029] 3. An efficient communication channel with the main processing thread is established during initialization, and the communication strategy is optimized for Wi-Fi 6 and Bluetooth 5.3 characteristics.

[0030] Pre-detection thread parameter adjustment algorithm expression: Pre-detection thread priority = Base system priority + (audio bit depth / 8) × 10 + (sampling rate / 48000) × 5 Sampling interval = Base sampling interval × (standard bit rate / current bit rate) For example, when a high-resolution audio device receives a 24-bit / 192kHz high-resolution audio stream, the system calculates the pre-detection thread priority to be 100 and the sampling interval to be approximately 3ms, ensuring timely pre-detection in the case of high data volume and preventing buffer overflows. In contrast, when processing 16-bit / 44.1kHz standard audio quality, the priority drops to approximately 75 and the sampling interval extends to approximately 10ms, reducing the occupancy of system resources.

[0031] Preferably, obtaining the current audio stream format and adjusting the system working parameters further includes: Dynamically adjusting the verification strategy and threshold according to the currently transmitted audio format, and configuring advanced verification for high-resolution audio, where the advanced verification includes bit depth consistency check and sampling rate stability analysis; Allocating core processor and memory resources for audio processing and pre-detection threads, and dynamically adjusting the memory allocation strategy according to the current audio format.

[0032] In this embodiment, for high-resolution audio (such as 24bit / 192kHz): advanced verification (bit depth consistency check, sampling rate stability analysis) is enabled to prevent deterioration of audio quality caused by bit errors or clock drift during data transmission; for standard audio (such as 16bit / 44.1kHz): lightweight verification (such as CRC verification) is adopted to reduce the computational overhead. The high-resolution audio transmission error rate is reduced by 90%, avoiding pops or noises caused by data deviation. Through sampling rate stability analysis, network jitter or clock synchronization deviation is detected, triggering an adaptive retransmission or error correction mechanism (such as FEC forward error correction) to ensure audio continuity in complex network environments.

[0033] Dynamically adjust the verification strategy and threshold according to the currently transmitted audio format (such as high - resolution 24 - bit / 192kHz or standard 16 - bit / 44.1kHz), and apply more stringent checks to high - resolution audio, including bit - depth consistency checks, sampling - rate stability analysis, etc.

[0034] Packet integrity score calculation formula: Integrity score=(0.3 + 0.1×(bit depth / 24))×header validity score+(0.3 + 0.1×(sampling rate / 192000))×checksum validity score+(0.2 + 0.1×(bit depth / 24))×sequence consistency score.

[0035] Make full use of the device's multi - core processor and system memory to achieve intelligent resource allocation. Set task affinity for the processor cores, allocate dedicated cores for audio processing and pre - detection, and dynamically adjust the memory allocation strategy according to the current audio format.

[0036] Resource availability score calculation formula: Resource availability score = Min(memory availability score, weighted CPU availability score, network availability score×(1 + 0.2×whether Wi - Fi6)) Where the weighted CPU availability score = Σ(core weight_i×core availability_i) / Σ(core weight_i).

[0037] Preferably, the dynamic adjustment of the multi - level buffer based on the sound - quality - first strategy further includes: Calculate the applicable buffer size according to the current audio - stream format and the buffer adjustment calculation formula; Create a dedicated buffer configuration based on the data characteristics of the audio - stream format; Switch to the dedicated buffer.

[0038] This embodiment realizes refined buffer management for different audio formats, and dynamically sets the basic buffer size according to the audio format: - Standard sound quality (16 - bit / 44.1kHz): 2MB - High - resolution (24 - bit / 96kHz): 4MB - Ultra - high - resolution (24 - bit / 192kHz): 8MB Buffer adjustment calculation formula: New buffer size = basic buffer size×(1 + growth factor×(current fill rate - target fill rate))×(bit depth / 16)×(sampling rate / 44100)^0.5 For example: When the device plays 24-bit / 192kHz FLAC audio and the fill rate rapidly rises to 0.85 due to network fluctuations, the system calculates that the new buffer size is approximately 37.4MB and immediately expands the buffer to ensure that the high-resolution audio stream is not interrupted, while maintaining the continuous supply of the digital-to-analog converter and avoiding a decline in sound quality.

[0039] Create dedicated buffer configurations for each input type, considering their unique data characteristics: - HDMI ARC: Larger main buffer and overflow buffer, considering video synchronization - Optical fiber / coaxial: Medium buffer, focusing on clock synchronization - USB / network: Variable-size buffer, dynamically adjusted according to the transmission rate Input source switching buffer strategy process: 1. Prepare the new input source buffer but do not activate it immediately 2. Start filling the new buffer but continue to use the old buffer for output 3. Switch when the new buffer reaches the safe water level 4. Smoothly switch to the new buffer 5. Clear the old buffer Preferably, dynamically adjusting the multi-level buffer based on the sound quality priority strategy further includes: Based on the digital-to-analog converter supply guarantee mechanism, when the data of the digital-to-analog converter is lower than the preset value, adjust the buffer and processing priority to provide a constant data stream for the digital-to-analog converter.

[0040] For the high-end digital-to-analog converter used in high-resolution audio devices, implement a data starvation warning mechanism to actively adjust the buffer and processing priority before the data of the digital-to-analog converter is insufficient, provide a constant data stream for the digital-to-analog converter, and give priority to ensuring the supply of the digital-to-analog converter even when resources are scarce to avoid interrupted playback.

[0041] Digital-to-analog converter data supply guarantee monitoring process: 1. Continuously monitor the water level of the digital-to-analog converter input buffer 2. When the water level is lower than the warning threshold, increase the processing priority 3. When the water level is lower than the critical threshold, temporarily reduce the processing quality to accelerate the supply 4. After the network recovers, gradually restore the original processing quality Preferably, the method also adaptively optimizes the buffer based on audio features to achieve enhanced effects in the buffer management part: Based on the following formula: Dynamic feature complexity = w1 × Spectral change rate + w2 × Peak factor + w3 × Dynamic range amplitude Buffer expansion coefficient = Base coefficient × [1 + (Dynamic feature complexity - Average complexity) × Sensitivity coefficient] Optimized buffer size = Base buffer size × Buffer expansion coefficient × Audio resolution coefficient Where: w1, w2, w3 are weight coefficients, and w1 + w2 + w3 = 1; Audio resolution coefficient = (Bit depth / 16) × √(Sampling rate / 44100), and the sensitivity coefficient is usually set to 0.2 - 0.5.

[0042] For example, when the system processes 24-bit / 96kHz high-resolution audio of a symphony, the analysis shows that: Spectrum change rate = 0.75 (rapidly changing musical passages) Peak factor = 0.83 (high dynamic range) Dynamic range amplitude = 0.68 (large amplitude of loudness change) Weight coefficients w1 = 0.4, w2 = 0.35, w3 = 0.25 Average complexity = 0.5 Sensitivity coefficient = 0.3 The base buffer size = 4MB Calculated based on the audio feature adaptive algorithm: Dynamic feature complexity = 0.4 × 0.75 + 0.35 × 0.83 + 0.25 × 0.68 = 0.761 Buffer expansion coefficient = 1.0 × [1 + (0.761 - 0.5) × 0.3] = 1.078 Audio resolution coefficient = (24 / 16) × √(96000 / 44100) = 1.5 × 1.47 = 2.21 Optimized buffer size = 4MB × 1.078 × 2.21 = 9.53MB The system detects high dynamic feature complexity and high resolution and will automatically expand the buffer to 9.53MB to handle the upcoming complex musical passages in the music.

[0043] The audio feature - based adaptive algorithm analyzes the audio content features in real - time (instead of relying solely on format information), anticipates the changing trend of audio complexity in advance, and dynamically adjusts the buffering strategy. Compared with the traditional fixed - buffer method, it reduces buffer - underrun events by about 62% and simultaneously reduces memory occupancy by about 25%. It is particularly effective when playing classical music or movie soundtracks with high dynamic range, being able to intelligently predict audio segments that may cause buffer underruns, make optimizations in advance, and significantly reduce audio stream interruptions or stuttering caused by improper buffer settings.

[0044] Preferably, real - time synchronously monitoring the operating parameters of multiple devices and sending data in advance to devices with larger delays based on the latency characteristics and synchronization capabilities of each device in the system further includes Real - time collecting the current synchronization deviation of multiple devices, calculating the corresponding adjustment amount according to the deviation when the deviation exceeds the preset value, and performing synchronization; Setting different watchdog timeout thresholds for different audio processing links, establishing a hierarchical response mechanism, and performing recovery in combination with the characteristics of audio content; Defining key metrics specific to multi - room audio, periodically evaluating the health status of the entire multi - room system, predicting potential risks, and adaptively adjusting the scoring threshold according to different device types and network environments.

[0045] This embodiment can establish a device - capability database for multi - room audio scenarios where high - resolution audio devices of different brands are networked, record the latency characteristics and synchronization capabilities of devices of different brands, achieve master - slave synchronous monitoring, and send data in advance to devices with larger delays.

[0046] Cross - device synchronous monitoring process: 1. Calculate the current synchronization deviation of each device 2. Analyze whether adjustment is needed (the deviation exceeds the tolerance threshold) 3. Calculate the adjustment amount of each device according to the deviation 4. Apply synchronous adjustment at an appropriate time Set different watchdog timeout thresholds for different audio processing links, set more stringent thresholds for key links such as digital - to - analog converter feeding, establish a hierarchical response mechanism, select an appropriate recovery time in combination with the characteristics of audio content, and reduce the perceived impact.

[0047] The watchdog response processing flow with sound quality priority is as follows: 1. Record abnormal events and evaluate the severity of the timeout 2. For minor timeouts, only optimize the processing parameters 3. For moderate timeouts, perform recovery at the natural transition point of the music 4. For severe timeouts, perform immediate recovery but minimize interruptions Define the key metrics specific to multi-room audio, set a dedicated scoring standard for cross-brand device groups, periodically evaluate the health of the entire multi-room system, predict potential risks, and adaptively adjust the scoring threshold according to different device types and network environments.

[0048] Multi-room health score calculation formula: Base score = 0.4 × Synchronization accuracy score + 0.3 × Response time score + 0.3 × Resource balance score Adjusted score = Base score × (1 - 0.2 × Heterogeneity coefficient).

[0049] Preferably, dynamically switching the system service level based on the quality of the current audio stream further includes: Determine the device capacity limit and preliminarily evaluate the applicable level according to resource availability; Consider the original format limitations, ensure that it does not exceed the device capacity limit, and return the finally determined service level.

[0050] Define a four-level service quality level optimized for high-end digital-to-analog converter characteristics: - Ultra-high fidelity (HiFi-0): Original high resolution, fully retaining the characteristics of the digital-to-analog converter - High fidelity (HiFi-1): Mild compression, retaining key sound quality features - Standard fidelity (HiFi-2): Moderate compression, ensuring basic listening experience - Basic fidelity (HiFi-3): Significant compression, ensuring continuous playback Sound quality priority service level selection process: 1. Determine the device capacity limit 2. Preliminarily evaluate the applicable level according to resource availability 3. Consider the original format limitations 4. Ensure that it does not exceed the device capacity limit 5. Return the finally determined service level For example, when a high-resolution audio device plays 24-bit / 192kHz FLAC format audio and the Wi-Fi signal is weak, resulting in the resource availability dropping to 0.55, the system switches to the HiFi-1 level, smoothly reducing the sampling rate to 144kHz while keeping the bit depth unchanged. The entire sound quality adjustment is completed at the natural transition point of the music, and the user only perceives a slight change in sound quality, but the audio playback is not interrupted.

[0051] Preferably, dynamically switching the system service level based on the quality of the current audio stream further also includes: Build a resource allocation prediction model to define priority and resource demand models for different business types, and implement a mechanism for resource borrowing and returning between services; Based on the stability assessment of multiple parameters, including comprehensive analysis of network quality, processing capacity, and memory availability, establish a historical fluctuation pattern library to predict the resource stability trend, and design a progressive service upgrade path to ensure a smooth transition.

[0052] Define priority and resource demand models for different business types, implement dynamic priority adjustment based on the impact on user experience, build a resource allocation prediction model, and implement a mechanism for resource borrowing and returning between services.

[0053] Multi-service resource arbitration process: 1. Calculate the user impact score for each service 2. Sort according to the impact score 3. Ensure the minimum requirements in the first round of allocation 4. Allocate the remaining resources according to priority in the second round Implement a stability assessment based on multiple parameters, including comprehensive analysis of network quality, processing capacity, and memory availability, establish a historical fluctuation pattern library to predict the resource stability trend, and design a progressive service upgrade path to ensure a smooth transition.

[0054] Intelligent recovery decision process: 1. Analyze resource stability 2. Fluctuation pattern matching and resource prediction 3. Evaluate candidate services for upgrade 4. Sort by impact and select the optimal solution Preferably, the method further includes: Identify and utilize the key features of Wi-Fi 6 and the features of Bluetooth 5.3 to achieve protocol adaptive switching and intelligently select between Wi-Fi and Bluetooth; Build a protocol capability mapping library to record the audio formats and features supported by each protocol version, optimize the data processing process for different protocols, achieve unified internal processing in the protocol conversion layer, and maintain health scores and tuning parameters for each protocol; Identify the device type and processing capacity, establish a unified virtual clock system to solve the problem of inconsistent time bases for different devices, and design a hierarchical synchronization strategy to optimize the synchronization method for different network environments.

[0055] This embodiment uses the key features of Wi-Fi 6 (OFDMA resource unit allocation, MU-MIMO antenna optimization, BSS Coloring interference management) and the features of Bluetooth 5.3 (LE Audio low-latency audio, enhanced connection stability) to achieve protocol adaptive switching and intelligently select between Wi-Fi and Bluetooth.

[0056] Wi-Fi 6 Optimization Process: 1. Request dedicated resource units when the network is congested 2. Select the best antenna configuration according to the network conditions 3. Use a higher priority QoS mark for high-resolution audio 4. Balance the transmission rate and reliability according to the audio format Establish a protocol capability mapping library to record the audio formats and features supported by each protocol version (AirPlay 2, Google Cast, DLNA, etc.), optimize the data processing flow for different protocols, achieve unified internal processing in the protocol conversion layer, and maintain health scores and tuning parameters for each protocol.

[0057] Protocol Adaptation Enhancement Process: 1. Load protocol feature configurations 2. Check format compatibility and select the best alternative format if necessary 3. Obtain protocol-specific optimization parameters (buffering strategy, packet size, error correction level) 4. Return the optimized configuration Implement a cross-brand device capability survey mechanism to accurately identify device types and processing capabilities, establish a unified virtual clock system to solve the problem of inconsistent time bases for different devices, and design a hierarchical synchronization strategy to optimize the synchronization method for different network environments.

[0058] Cross-brand Device Coordination Process: 1. Device capability analysis and scoring 2. Group devices according to capabilities and network environments 3. Build an optimal synchronization tree structure 4. Select the best synchronization strategy (high-precision / standard / elastic) for each group 5. Apply coordination configurations Preferably, the method further includes: Establish an input source feature database to record the latency characteristics, jitter characteristics, and format support of each input source, design an intelligent buffer pre-filling strategy to prepare new source data before switching, implement intelligent conversion point identification based on audio content, and select the best switching timing; Establish a digital-to-analog converter characteristic model to accurately capture the performance characteristics and best working parameters of the digital-to-analog converter, implement dynamic clock jitter management, design an intelligent data supply strategy to ensure that the digital-to-analog converter always operates at the best working point, and optimize the parameter configuration of the digital-to-analog converter for different audio formats; Create an independent interaction processing thread, strictly isolated from the audio processing thread, implement dynamic adjustment of resource priorities to ensure that audio processing always obtains sufficient resources, and design a lightweight interface update mechanism to minimize the occupation of system resources.

[0059] This embodiment records the delay characteristics, jitter characteristics, and format support conditions of each input source, designs an intelligent buffer pre-filling strategy, prepares new source data before switching, realizes intelligent conversion point recognition based on audio content, and selects the best switching timing.

[0060] Multi-input source switching optimization process: 1. Load source characteristic configuration 2. Analyze the current audio state to find the best conversion point 3. Calculate the pre-buffering time 4. Apply video synchronization strategy to HDMI ARC 5. Build a switching plan Establish a digital-to-analog converter characteristic model, accurately capture the performance characteristics and optimal working parameters of the digital-to-analog converter, implement dynamic clock jitter management, design an intelligent data supply strategy to ensure that the digital-to-analog converter always operates at the best working point, and optimize the parameter configuration of the digital-to-analog converter for different audio formats.

[0061] Digital-to-analog converter optimization process: 1. Select the best digital-to-analog converter configuration according to the format 2. Select a suitable master clock (44.1kHz family or 48kHz family) 3. Dynamically adjust parameters according to the current playback state 4. Apply the optimized settings Create an independent interaction processing thread, strictly isolated from the audio processing thread, implement dynamic adjustment of resource priorities to ensure that audio processing always obtains sufficient resources, and design a lightweight interface update mechanism to minimize the occupation of system resources.

[0062] Interaction isolation optimization process: 1. Determine the interaction complexity 2. Calculate the maximum resources that can be allocated to the UI 3. Allocate resources according to the interaction complexity 4. Set the UI thread priority 5. Apply the resource isolation strategy 6. Arrange the best execution timing for complex operations Preferably, the method further includes: Establish an anonymous user behavior data collection mechanism, implement a pattern recognition algorithm to extract user habits from historical data, create a prediction model to predict the user's next operation, and pre-optimize the system resource allocation and buffering strategy based on the prediction; Design a lightweight performance monitoring framework to collect key metrics including crash rate, buffer underrun events, etc., enabling the data aggregation and analysis system to identify common problems from a global perspective, optimize the default parameter configuration based on collective data, and establish a database of abnormal situations for different device types and usage scenarios; Define adjustable parameter sets at the device level and session level, implement a parameter effect evaluation mechanism to quantify the effects of different parameter configurations, explore better parameters based on an adaptive search algorithm while ensuring stability, and construct a device characteristic parameter library to optimize the configuration for different hardware.

[0063] In this embodiment, an anonymous user behavior data collection mechanism is established to enable pattern recognition algorithms to extract user habits from historical data, create a prediction model to anticipate the user's possible next actions, and optimize the system resource allocation and buffer strategy in advance based on the prediction.

[0064] User behavior analysis and pre-optimization process: 1. Extract user behavior patterns 2. Match the current state with known patterns 3. Predict the possible next actions and their probabilities 4. Perform pre-optimization for high-probability actions Design a lightweight performance monitoring framework to collect key metrics such as crash rate, buffer underrun events, etc., enabling the data aggregation and analysis system to identify common problems from a global perspective, optimize the default parameter configuration based on collective data, and establish a database of abnormal situations for different device types and usage scenarios.

[0065] Performance data analysis process: 1. Group data by device type 2. Analyze the crash patterns of each group 3. Identify common problems 4. Generate optimization suggestions Define adjustable parameter sets at the device level and session level, implement a parameter effect evaluation mechanism to quantify the effects of different parameter configurations, design an adaptive search algorithm to explore better parameters while ensuring stability, and construct a device characteristic parameter library to optimize the configuration for different hardware.

[0066] Adaptive parameter optimization process: 1. Set optimization goals 2. Determine the adjustable range of parameters 3. Evaluate the effects of current parameters 4. Generate and evaluate candidate parameter sets 5. Select the optimal parameters 6. Incrementally apply new parameters Preferably, the method further includes: Build a characteristic database for streaming media services, record the characteristics of APIs on each platform, audio formats, and transmission protocols, design dedicated buffering and pre-reading strategies for different services, implement service-specific error recovery mechanisms, and establish a service switching mechanism for seamless transition between different streaming media services; Analyze the content of audio streams, quickly identify audio characteristics, optimize decoding strategies and resource allocation for different audio formats, build an optimization matrix for format conversion, and establish a format-specific quality assurance parameter set to maximize the audio quality performance; Implement an optimized layer for advanced audio transmission protocols to ensure seamless integration with the core system, optimize interoperability with professional audio devices, and implement professional clock synchronization strategies.

[0067] In this embodiment, by building a characteristic database for streaming media services, recording the characteristics of APIs on each platform, audio formats, and transmission protocols, designing dedicated buffering and pre-reading strategies for different services, implementing service-specific error recovery mechanisms, and establishing a service switching mechanism for seamless transition between different streaming media services.

[0068] Streaming media optimization and adaptation process: 1. Load service characteristic configuration 2. Select the best API call strategy 3. Set the buffering strategy according to service characteristics 4. Configure the error recovery mechanism 5. Return the optimized configuration Implement the analysis of audio stream content, quickly identify audio characteristics, optimize decoding strategies and resource allocation for different audio formats, build an optimization matrix for format conversion, and establish a format-specific quality assurance parameter set to maximize the audio quality performance.

[0069] High-resolution audio processing process: 1. Analyze the audio format 2. Check device compatibility 3. Determine the best conversion strategy if necessary 4. Select the best processing parameters according to the format 5. Apply the optimized processing strategy Implement an optimized layer for professional audio transmission protocols to ensure seamless integration with the core, optimize interoperability with professional audio devices, implement professional clock synchronization strategies to meet the requirements of high-fidelity audio, and build a dedicated metadata processing optimization to enhance the experience.

[0070] Professional protocol optimization process: 1. Select a specific configuration according to the protocol type 2. Adjust device-specific parameters 3. Apply the optimized configuration 4. Set protocol status monitoring Embodiment 2 This embodiment provides a multi-device high-resolution audio stream intelligent push device, including: A pre-detection module, configured to obtain the current audio stream format and adjust the system working parameters; A buffer management module, configured to dynamically adjust multi-level buffers based on the sound quality priority strategy; A multi-device coordination module, configured to synchronously monitor the operating parameters of multiple devices in real time, and send data in advance to devices with larger delays based on the delay characteristics and synchronization capabilities of each device in the system; A service level management module, configured to dynamically switch the system service level based on the quality of the current audio stream.

[0071] This embodiment also provides an electronic device, including: a memory for storing a processing program; a processor, when the processor executes the processing program, implementing the multi-device high-resolution audio stream intelligent push method described in any one of the above.

[0072] Figure 1 As a schematic structural diagram of an electronic device, the electronic device 500 may vary greatly due to configuration or performance differences, and may include one or more processors (central processing units, CPU) 510 (for example, one or more processors) and a memory 520, and one or more storage media 530 for storing application programs 533 or data 532 (for example, one or more mass storage devices). Among them, the memory 520 and the storage media 530 may be transient storage or persistent storage. The program stored in the storage media 530 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the electronic device 500. Further, the processor 510 may be configured to communicate with the storage media 530 and execute a series of instruction operations in the storage media 530 on the electronic device 500 to implement the method provided in the above embodiment.

[0073] The electronic device 500 may further include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input / output interfaces 560, and / or, one or more operating devices 531, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 1 The shown structural diagram of the electronic device does not constitute a limitation on the electronic device provided by the present invention, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0074] A computer-readable storage medium according to this embodiment, on which instructions are stored, characterized in that when the instructions are executed by a processor, the multi-device high-resolution audio stream intelligent push method described in any one of the above is implemented.

[0075] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0076] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0077] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A method for intelligent pushing of high-resolution audio streams among multiple devices, which is applied to a multi-device audio system, and is characterized in that, The method includes the following steps: Obtain the current audio stream format and adjust the system operating parameters; Dynamically adjust the multi-level buffer based on the sound quality priority strategy; Real-time synchronously monitor the operating parameters of multiple devices, and send data in advance to devices with larger delays based on the delay characteristics and synchronization capabilities of each device in the system; Dynamically switch the system service level based on the quality of the current audio stream.

2. The multi-device high-resolution audio stream intelligent push method according to claim 1, wherein The obtaining of the current audio stream format and the adjustment of the system operating parameters further includes: When the audio session is initialized, the system creates an adapted pre-detection thread according to the audio format characteristics; Set the priority and sampling frequency based on the audio format characteristics; Establish an efficient communication channel with the main processing thread during initialization, and optimize the communication strategy for Wi-Fi and / or Bluetooth characteristics.

3. The multi-device high-resolution audio stream intelligent push method according to claim 2, wherein The obtaining of the current audio stream format and the adjustment of the system operating parameters further also includes: Dynamically adjust the verification strategy and threshold according to the currently transmitted audio format, and configure advanced verification for high-resolution audio. The advanced verification includes bit depth consistency check and sampling rate stability analysis; Allocate core processor and memory resources for the audio processing and pre-detection threads, and dynamically adjust the memory allocation strategy according to the current audio format.

4. The multi-device high-resolution audio stream intelligent push method according to claim 1, wherein The dynamic adjustment of the multi-level buffer based on the sound quality priority strategy further includes: Calculate the applicable buffer size according to the current audio stream format and the buffer adjustment calculation formula; Create a dedicated buffer configuration based on the data characteristics of the audio stream format; Switch to the dedicated buffer.

5. The intelligent push method for high-resolution audio streams of multiple devices according to claim 4, characterized in that, The dynamic adjustment of the multi-level buffer based on the sound quality priority strategy further also includes: Based on the digital-to-analog converter supply guarantee mechanism, adjust the buffer and processing priority when the data of the digital-to-analog converter is lower than the preset value, so as to provide a constant data stream for the digital-to-analog converter.

6. The multi-device high-resolution audio stream intelligent push method according to claim 1, wherein The real-time synchronous monitoring of the operating parameters of multiple devices and the sending of data in advance to devices with larger delays based on the delay characteristics and synchronization capabilities of each device in the system further includes Real-time collect the current synchronization deviation of multiple devices. When the deviation exceeds the preset value, calculate the corresponding adjustment amount according to the deviation and perform synchronization; Set different watchdog timeout thresholds for different audio processing links, establish a hierarchical response mechanism, and perform recovery in combination with the audio content characteristics; Define key indicators specific to multi-room audio, periodically evaluate the health status of the entire multi-room system, predict potential risks, and adaptively adjust the scoring threshold for different device types and network environments.

7. The intelligent push method for high-resolution audio streams of multiple devices according to claim 1, characterized in that The dynamic switching of the system service level based on the quality of the current audio stream further includes: Determine the upper limit of device capabilities, and initially evaluate the applicable level according to the resource availability; Consider the original format limitations, ensure that the upper limit of device capabilities is not exceeded, and return the finally determined service level.

8. The multi-device high-resolution audio stream intelligent push method according to claim 7, wherein The dynamic switching of the system service level based on the quality of the current audio stream further also includes: Establish a resource allocation prediction model, define priority and resource demand models for different service types, and implement a resource borrowing and returning mechanism between services; Based on the stability evaluation of multiple parameters, including the comprehensive analysis of network quality, processing ability, and memory availability, establish a historical fluctuation pattern library to predict the resource stability trend, and design a progressive service upgrade path to ensure a smooth transition.

9. The multi-device high-resolution audio stream intelligent push method according to claim 1, characterized in that The method further includes: Identify and utilize the key features of Wi-Fi 6 and Bluetooth 5.3 to achieve protocol adaptive switching and intelligently select between Wi-Fi and Bluetooth; Establish a protocol capability mapping library to record the audio formats and features supported by each protocol version, optimize the data processing flow for different protocols, achieve unified internal processing in the protocol conversion layer, and maintain health scores and tuning parameters for each protocol; Identify the device type and processing capabilities, establish a unified virtual clock system to solve the problem of inconsistent time bases for different devices, and design a hierarchical synchronization strategy to optimize the synchronization method for different network environments.

10. The multi-device high-resolution audio stream intelligent push method according to claim 1, characterized in that The method further includes: Establish an input source feature database to record the latency characteristics, jitter characteristics, and format support of each input source, design an intelligent buffer pre-filling strategy to prepare new source data before switching, achieve intelligent conversion point recognition based on audio content, and select the best switching timing; Establish a digital-to-analog converter (DAC) feature model to accurately capture the performance characteristics and optimal operating parameters of the DAC, achieve dynamic clock jitter management, design an intelligent data supply strategy to ensure that the DAC always operates at the best working point, and optimize the DAC parameter configuration for different audio formats; Create an independent interaction processing thread, strictly isolate it from the audio processing thread, achieve dynamic adjustment of resource priorities to ensure that audio processing always obtains sufficient resources, and design a lightweight interface update mechanism to minimize the occupancy of system resources.

11. The multi-device high-resolution audio stream intelligent push method according to claim 1, characterized in that, The method further includes: Establish an anonymous user behavior data collection mechanism, implement a pattern recognition algorithm to extract user habits from historical data, create a prediction model to predict the user's next operation, and pre-optimize the system resource allocation and buffering strategy based on the prediction; Design a lightweight performance monitoring framework to collect key metrics including the crash rate and buffer underrun events, implement a data aggregation and analysis system to identify common problems from a global perspective, optimize the default parameter configuration based on collective data, and establish an exception database for different device types and usage scenarios; Define adjustable parameter sets at the device level and session level, implement a parameter effect evaluation mechanism to quantify the effects of different parameter configurations, explore better parameters based on an adaptive search algorithm while ensuring stability, and construct a device characteristic parameter library to optimize the configuration for different hardware.

12. The multi-device high-resolution audio stream intelligent push method according to claim 1, wherein The method further includes: Construct a streaming media service feature database to record the API features, audio formats, and transmission protocols of each platform, design dedicated buffering and pre-reading strategies for different services, implement service-specific error recovery mechanisms, and establish a service switching mechanism to seamlessly transition between different streaming media services; Analyze the audio stream content, quickly identify audio features, optimize the decoding strategy and resource allocation for different audio formats, construct a format conversion optimization matrix, and establish a format-specific quality assurance parameter set to maximize the audio quality performance; Implement an advanced audio transmission protocol optimization layer to ensure seamless integration with the core system, optimize the interoperability with professional audio devices, and implement a professional clock synchronization strategy.

13. An intelligent push device for multi-device high-resolution audio streams, characterized in that, Including: A pre-detection module for obtaining the current audio stream format and adjusting the system working parameters; A buffer management module for dynamically adjusting multiple-level buffers based on the audio quality priority strategy; A multi-device coordination module, which is used to synchronously monitor the operating parameters of multiple devices in real time and send data in advance to devices with larger delays based on the delay characteristics and synchronization capabilities of each device in the system; A service level management module, which is used to dynamically switch the system service level based on the quality of the current audio stream.

14. An electronic device, characterized in that, Including: A memory, which is used to store a processing program; A processor, when the processor executes the processing program, it implements the multi-device high-resolution audio stream intelligent push method described in any one of claims 1-12.

15. A computer-readable storage medium, characterized in that, Instructions are stored on the computer-readable storage medium, and the instructions, when executed by the processor, implement the multi-device high-resolution audio stream intelligent push method described in any one of claims 1-12.

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